Assessing the Level of Poverty and Utilization of Government Social Programs Among Tobacco Farmers in Indonesia
Bibliographic record
Abstract
INTRODUCTION: Studies examining profit suggest that former tobacco farmers do as well or better than current tobacco farmers. Research has yet to examine the relationship among current and former tobacco farmers, poverty, and receipt of government social assistance. This type of research is critical to understanding the direct and indirect subsidization of tobacco growing. This study analyzed tobacco farmers' poverty levels and receipt of government social assistance programs. AIMS AND METHODS: We designed and conducted an original four-wave economic survey of current and former tobacco farming households in Indonesia between 2016 and 2022. We then used descriptive analysis and probit regression for panel data to estimate the relationship between tobacco farming and poverty status. RESULTS: Tobacco farmers' per capita income and poverty rates vary across years. The poverty rate was significantly higher in the year with a higher-than-normal rainfall as it negatively affected farming outcomes. During this year, the poverty rate among current tobacco farmers was also higher than that of former tobacco farmers. Regression estimates from the panel data confirm the association between tobacco farming and the likelihood of being poor. We also found a high share of current tobacco farmers who receive government social assistance programs, such as cash transfer programs and a universal healthcare program. CONCLUSIONS: Our findings show high poverty rates-particularly during bad farming years-and high rates of government social assistance among tobacco farmers. The high rates of government assistance among tobacco farmers living in poverty show that the government is indirectly subsidizing the tobacco industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".